The rapid development of computer technologies, especially of web technologies, has brought about a rapid increase in the computers' capability of information storage, transmission and processing. Conceptual Model like Ontology Description Language (RDF/OWL), Entity-Relationship (ER) model and Object-Oriented (OO) model describes information systems from both semantic and knowledge perspectives and provides a common framework that allows data to be shared and reused across different applications and enterprises. It is an extension of the current web, in which information is given well-defined meaning, and which enables computers and users to effectively work in cooperation. RDF and OWL are Semantic Web standards that provide a framework for asset management, enterprise integration and the sharing and reuse of data on the Web; ER model is a conceptual data model that views the real world as entities and relationships; and OO model has become the de-facto standard in the early phase of a software development process during the last decade. The current state-of-art is dominated by the Unified Modeling Language (UML), the development of which has been initiated and pushed by industry. As a language used for software system description and construction and business modeling, UML has synthesized excellent software engineering methods that have won approval in the modeling field for large and complex systems.
With the popularization and maturity of various Conceptual Model technologies, more and more domain knowledge has been built by using Conceptual Model technologies in order to be shared and reused across web and applications. On the one hand, extensional knowledge (rules) of a domain, such as the objects, concepts and other entities that are assumed to exist in some area, is represented by Conceptual Model like RDF/OWL (Ontology), ER model, OO model, etc. On the other hand, considering that Relational Databases, over Conceptual Model objects like RDF triples, has the better stability of stored data and the better maturity of the storage technology, intentional knowledge (facts) is usually stored in Relational Databases (RDB) as RDB records. That is to say, the corresponding domain facts (intentional knowledge) repository is more commonly implemented in the form of Relational Databases (RDB).
As a standard query language to Relational Database, structured query language SQL is a language between relational algebra and tuple calculus. Since various types of computer and database systems have used SQL as their access language and standard interface after it became an international standard, the database world might be linked as a unified body. This prospect is of great significance. However, faced with huge and complex stored information in database systems, people, especially product salesmen, financial advisers, risk evaluators, enterprise policy-makers and other non-IT professionals, hesitate to use SQL to query data stored in RDB due to not only its complexity and error-proneness but also its lack of descriptive capability. As a result, non-IT professionals can hardly express their desirable query target via SQL.
Additionally, RDB can be accessed via the Extensional Markup Language (XML) in the prior art. Such a solution provides extended XML-supported modules on the basis of RDB, which temporarily stores XML data in RDB and transforms XML data query language into RDB query language, namely SQL, during a query. Its advantage lies in making full use of the mature technology of conventional databases. However, in this kind of solutions, queries are implemented by composing XML command lines, and it is still difficult for those non-IT professionals to conduct an effective query.
In another prior art, a highly effectively deductive database system can be constructed by adding a rule processing layer on the basis of an RDB system. That is to say, this kind of systems mainly comprises an RDB management system and a rule reasoning management system. The advantage of such a deductive database lies in the capability of effective deduction and reasoning based on user queries to obtain query results. However, users have to edit logic programs as query inputs. Therefore, effective queries are still hardly achieved by those non-IT professionals.
As described above, in the prior arts, knowledge to a domain is able to be captured as Conceptual Model and Business Rules, and subsequently Conceptual Model and Business Rules can be combined to be as a concept-based query language (CQL). CQL inherits the features from both Conceptual Model and Business Rules, such as concept subsumption, concept relation, recursiveness, stratified negation and program with multiple rules. In fact, such query language as CQL cannot be utilized in query systems provided by the prior arts to query the corresponding repository of domain facts that is implemented in the form of RDB. As a result, those non-IT professionals can hardly make effective use of knowledge stored in repositories of domain facts.